Automatically classifying an object (with its set of feature values) as an adequate representative of a particular category is a difficult problem when the values of individual features are not sufficient to discriminate among categories, nor are linear combinations of feature values sufficient for accurate discrimination. The automated classification task is particularly important for automated speech recognition, and the evaluation of speech pronunciation. One conventional method for automated classification is to measure the distance of the object's features to the features of an exemplar of the category. If that distance is less than some threshold, and the distance is less than to members of other categories, then the original object is an adequate representative of the first category. This is known as the “distance-threshold” methodology, and is sometimes also called the “k-nearest-neighbors” method. This methodology, whether or not it relies only on distance to the target category or also on distance to members of other categories, assumes that the exemplar's features were measured under the same conditions as the object's features, which is not necessarily always the case. An embodiment of the present invention can normalize the features of the exemplar.
Pronouncing the phonemes of a language accurately is important for producing intelligible speech. Phonemes are the smallest units of recognizable speech sounds, typically smaller than a syllable. For example, the word MAN has three phonemes: the M sound, the vowel sound in the middle, and the final N sound.
Evaluating pronunciation of phonemes, for purposes of teaching a foreign language, providing speech therapy, etc. is made difficult because of acoustic noise in recording speech for evaluation and the natural, acceptable variations in speech. An embodiment of the present invention is a system and method and computer program product for evaluating a user's pronunciation of phonemes in a language that minimizes the impact of such acoustic noise and variations in speech.